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Shape classification using spectral graph wavelets
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DOI：10.1007/s10489-017-0955-7.png)
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Abstract
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Spectral shape descriptors have been used extensively in a broad spectrum of geometry processing applications ranging from shape retrieval and segmentation to classification. In this paper, we propose a spectral graph wavelet approach for 3D shape classification using the bag-of-features paradigm. In an effort to capture both the local and global geometry of a 3D shape, we present a three-step feature description framework. First, local descriptors are extracted via the spectral graph wavelet transform having the Mexican hat wavelet as a generating kernel. Second, mid-level features are obtained by embedding local descriptors into the visual vocabulary space using the soft-assignment coding step of the bag-of-features model. Third, a global descriptor is constructed by aggregating mid-level features weighted by a geodesic exponential kernel, resulting in a matrix representation that describes the frequency of appearance of nearby codewords in the vocabulary. Experimental results on two standard 3D shape benchmarks demonstrate the effectiveness of the proposed classification approach in comparison with state-of-the-art methods.
Keywords:
Spectral graph wavelet
Laplace-Beltrami
Bag-of-features
Support vector machines
Shape descriptors
Classification
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